کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
559200 1451864 2015 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Wear detection by means of wavelet-based acoustic emission analysis
ترجمه فارسی عنوان
تشخیص با استفاده از تجزیه و تحلیل انتشار آکوستیک مبتنی بر موجک
کلمات کلیدی
سایش کشویی، تشخیص سایش مبتنی بر سیگنال، انتشار آکوستیک، دسته بندی خودکار پوششی تبدیل موجک مداوم
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی


• Real-time investigation of surface damage using acoustic emission.
• Automated classification of three wear phases.
• Establishing a correlation between state-of-wear and AE features.
• Complex AE-feature generation based on Wavelet.
• Identification of specific frequencies related to the different wear states.

Wear detection and monitoring during operation are complex and difficult tasks especially for materials under sliding conditions. Due to the permanent contact and repetitive motion, the material surface remains during tests non-accessible for optical inspection so that attrition of the contact partners cannot be easily detected. This paper introduces the relevant scientific components of reliable and efficient condition monitoring system for online detection and automated classification of wear phenomena by means of acoustic emission (AE) and advanced signal processing approaches. The related experiments were performed using a tribological system consisting of two martensitic plates, sliding against each other. High sensitive piezoelectric transducer was used to provide the continuous measurement of AE signals. The recorded AE signals were analyzed mainly by time-frequency analysis. A feature extraction module using a novel combination of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were used for the first time. A detailed correlation analysis between complex signal characteristics and the surface damage resulting from contact fatigue was investigated. Three wear process stages were detected and could be distinguished. To obtain quantitative and detailed information about different wear phases, the AE energy was calculated using STFT and decomposed into a suitable number of frequency levels. The individual energy distribution and the cumulative AE energy of each frequency components were analyzed using CWT. Results show that the behavior of individual frequency component changes when the wear state changes. Here, specific frequency ranges are attributed to the different wear states. The study reveals that the application of the STFT-/CWT-based AE analysis is an appropriate approach to distinguish and to interpret the different damage states occurred during sliding contact. Based on this results a new generation of condition monitoring systems can be build, able to evaluate automatically the surface condition of machine components with sliding surfaces.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volumes 60–61, August 2015, Pages 198–207
نویسندگان
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